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94 is in the network. Such techniques have applications, for instance, in social networks used to verify the trustworthiness or relevance of received information [57]. 5.8 Tightly clustered fringe Next, we consider the graph properties at the scale of local neighborhoods outside of the core. We first examine clustering, which quantifies how densely the neighborhood of a node is connected. Ratio to Random Graphs Network Web [3] Flickr LiveJournal Orkut YouTube C 0.081 0.313 0.330 0.171 0.136 Erd ̈os-R ́enyi Power-Law 7.71 - 47,200 25.2 119,000 17.8 7,240 5.27 36,900 69.4 Table 5.4 : The observed clustering coefficient, and ratio to random Erd ̈os-R ́eyni graphs as well as random power-law graphs. Table 5.4 shows the clustering coefficients for all four social networks. For compar- ison, we show the ratio of the observed clustering coefficient to that of Erd ̈os-R ́eyni (ER) random graphs [48] and random power-law graphs constructed with preferential attachment [15], with the same number of nodes and links. ER graphs have no link bias towards local nodes. Hence, they provide a point of reference for the degree of local clustering in the social networks. Graphs constructed using preferential attach-PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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